In many applications and customer devices, before displaying images, pictures, or frames the underlying images, frames, or pictures and the respective data have been pre-processed in order to better adapt the image properties, and the like. One aspect is the enhancement of sharpness of the image, picture, or frame. A problem when enhancing the sharpness of an image or with respect to other pre-processing steps is that the respective process is in general applied to all image details represented by the image data. However, some of the image details indeed originate from noise components which are also contained in the image and the image data. Under a process of e.g. sharpness enhancement, also respective noise components contained in the image data will be enhanced and therefore will be amplified.
It is therefore necessary to know in advance, i.e. before pre-processing image data, to know something about the general noise level in the respective image, in particular with respect to homogeneous and textured regions of the image.
In addition, for several aspects of digital picture or digital image processing and their applications, for instance in consumer or customer devices, it is important for the respective processing or even for the application to have an estimate of the respective noise level contained in the respective images, the pictures or the respective signals conveying the same. This is in particular important for so-called noise compensation processes, for instance in image enhancement processing.
Many known noise estimation methods are comparable complicated and time-consuming on the one hand and have on the other hand problems in analysing the underlying image or picture material, i.e. it is not obvious which part of an image or of a sequence of images has to be analysed in order to obtain a realistic estimate of the noise level.